THE ECONOMIC POTENTIAL OF GENERATIVE AI: THE INNOVATIVE PRODUCTIVITY AREA INTRODUCTION

… Along with a handful of colleagues, Mr. Moore could claim credit for bringing laptop computers to hundreds of millions of people and embedding microprocessors into everything from bathroom scales, toasters and toy fire engines to cellphones, cars and jets. … In 1965, in what became known as Moore’s Law, he predicted that the number of transistors that could be placed on a silicon chip would double at regular intervals for the foreseeable future, thus increasing the data-processing power of computers exponentially. Generative AI tools like ChatGPT reached mass adoption in record time, and reset the course of an entire industry. VentureBeat’s mission is to be a digital town square for technical decision-makers to gain knowledge about transformative enterprise technology and transact.

• And in May 2023, Google announced several new features powered by generative AI, including Search Generative Experience and a new LLM, called PaLM 2 that will power its Bard chatbot, among other Google products.
• We expect this space to evolve rapidly and will continue to roll out our research as that happens.
• Optimizing inventory management and recommending products to customers based on their purchase history and browsing behavior is only part of the value of gen AI in the retail industry.
• But business leaders should begin implementing generative AI use cases as soon as possible rather than waiting on the sidelines as the performance gap between laggards and early adopters will widen quickly.
• The third example is pharma and medical products, with an estimated total value per industry of $60 billion–$110 billion, and a value potential increase of 15–25% of operating profits based on average profitability of selected industries in the 2020–22 period.

Generative AI has the potential to revolutionize the entire customer operations function, improving the customer experience and agent productivity through digital self-service and enhancing and augmenting agent skills. The technology has already gained traction in customer service because of its ability to automate interactions with customers using natural language. Crucially, productivity and quality of service improved most among less-experienced agents, while the AI assistant did not increase—and sometimes decreased—the productivity and quality metrics of more highly skilled agents. This is because AI assistance helped less-experienced agents communicate using techniques similar to those of their higher-skilled counterparts. With the acceleration in technical automation potential that generative AI enables, our scenarios for automation adoption have correspondingly accelerated.

Gen AI represents just a small piece of the value potential from AI

Documenting code functionality for maintainability (which considers how easily code can be improved) can be completed in half the time, writing new code in nearly half the time, and optimizing existing code (called code refactoring) in nearly two-thirds the time. Since the release of ChatGPT in November 2022, it’s been all over the headlines, and businesses are racing to capture its value. Within the technology’s first few months, McKinsey research found that generative AI (gen AI) features stand to add up to $4.4 trillion to the global economy—annually. Optimizing inventory management and recommending products to customers based on their purchase history and browsing behavior is only part of the value of gen AI in the retail industry.

Gen AI is expected to play a role in improving the quality, safety, efficiency, and sustainability of future transportation systems that do not exist today. Gen AI is expected to help address this shortage through increased efficiency, allowing fewer workers to serve more patients. Briggs also previously worked as an economist with the Vanguard Research Initiative and taught at Johns Hopkins University and New York University. An important phase of drug discovery involves the identification and prioritization of new indications—that is, diseases, symptoms, or circumstances that justify the use of a specific medication or other treatment, such as a test, procedure, or surgery. Possible indications for a given drug are based on a patient group’s clinical history and medical records, and they are then prioritized based on their similarities to established and evidence-backed indications. One European bank has leveraged generative AI to develop an environmental, social, and governance (ESG) virtual expert by synthesizing and extracting from long documents with unstructured information.

So understanding the use cases that will deliver the most value to your industry is key

AI has been driving value for businesses since the early 2000s; however, the majority of AI models have been discriminative, not generative. Discriminative models excel at making predictions from existing data and identifying anomalies. These models power everything from social media content recommendation engines to financial fraud detection platforms. However, these models output only discrete results—for example, “This transaction is likely fraudulent.» Generative AI systems like ChatGPT have shifted the paradigm from classifying information to creating an array of novel outputs, thus greatly expanding the set of tasks AI can perform. The large language model (LLM) released by OpenAI is the first program to make generative artificial intelligence (AI) easily accessible to the public.

As a result of these reassessments of technology capabilities due to generative AI, the total percentage of hours that could theoretically be automated by integrating technologies that exist today has increased from about 50 percent to 60–70 percent. The technical potential curve is quite steep because of the acceleration in generative AI’s natural-language capabilities. Generative AI tools can draw on existing documents and data sets to substantially streamline content generation. These tools can create personalized marketing and sales content tailored to specific client profiles and histories as well as a multitude of alternatives for A/B testing. In addition, generative AI could automatically produce model documentation, identify missing documentation, and scan relevant regulatory updates to create alerts for relevant shifts. First, they can draft code based on context via input code or natural language, helping developers code more quickly and with reduced friction while enabling automatic translations and no- and low-code tools.

While we cannot predict the future, it is likely that generative AI will serve as a “copilot” that augments people’s ability to perform their roles, thereby leading an evolution of tasks within roles rather than eliminating jobs altogether. For example, the Access Partnership research projects that 45% of workers in India will potentially use generative AI for up to 20% of regular work activities. Our research found that equipping developers with the tools they need to be their most productive also significantly improved their experience, which in turn could help companies retain their best talent. Developers using generative AI–based tools were more than twice as likely to report overall happiness, fulfillment, and a state of flow. They attributed this to the tools’ ability to automate grunt work that kept them from more satisfying tasks and to put information at their fingertips faster than a search for solutions across different online platforms. When we had 40 of McKinsey’s own developers test generative AI–based tools, we found impressive speed gains for many common developer tasks.

• Such virtual expertise could rapidly “read” vast libraries of corporate information stored in natural language and quickly scan source material in dialogue with a human who helps fine-tune and tailor its research, a more scalable solution than hiring a team of human experts for the task.
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• The first example is banking, with an estimated total value per industry of $200 billion to $340 billion, and a value potential increase of 9–15% of operating profits based on average profitability of selected industries in the 2020–22 period.
• We then estimated the potential annual value of these generative AI use cases if they were adopted across the entire economy.

While the premium is on vertical integration in the earliest days of a new, transformative technology, as the architecture matures, innovation shifts to its new and evolving component parts, or modules. By virtue of what economists call the “mirroring hypothesis,” this evolution will ultimately lead to the modularization of the GenAI industry as a whole. The economic consequence of modularity is that it effectively redistributes the industry profit pools that are currently clustered around the foundational models and the tech companies that made them, creating multiple loci of innovation along the value chain. For the economic potential of generative ai marketing and sales, McKinsey found that creating more personalized and intelligent content with GenAI “could increase the productivity of the marketing function with a value between 5 and 15% of total marketing spending,” and increase the productivity of sales spending 3 to 5% globally. Analyzing databases detailing the task content of over 900 occupations, our economists estimate that roughly two-thirds of U.S. occupations are exposed to some degree of automation by AI. They further estimate that, of those occupations that are exposed, roughly a quarter to as much as half of their workload could be replaced.

Gen AI could ultimately boost global GDP

Generative AI has shown the potential to automate routine tasks, enhance risk mitigation, and optimize financial operations. The tools — some of which can also generate images and video, and carry on a conversation — have started a debate over how they will affect jobs and the world economy. Will displace people from their work, while others have said the tools can augment individual productivity. Labor economists have often noted that the deployment of automation technologies tends to have the most impact on workers with the lowest skill levels, as measured by educational attainment, or what is called skill biased. We find that generative AI has the opposite pattern—it is likely to have the most incremental impact through automating some of the activities of more-educated workers (Exhibit 12).

The share of organizations that have adopted AI overall remains steady, at least for the moment, with 55 percent of respondents reporting that their organizations have adopted AI. Less than a third of respondents continue to say that their organizations have adopted AI in more than one business function, suggesting that AI use remains limited in scope. Product and service development and service operations continue to be the two business functions in which respondents most often report AI adoption, as was true in the previous four surveys. And overall, just 23 percent of respondents say at least 5 percent of their organizations’ EBIT last year was attributable to their use of AI—essentially flat with the previous survey—suggesting there is much more room to capture value.

At the same time, they also have the potential to be more destabilizing than previous generations of artificial intelligence. They are capable of that most human of abilities, language, which is a fundamental requirement of most work activities linked to expertise and knowledge as well as a skill that can be used to hurt feelings, create misunderstandings, obscure truth, and incite violence and even wars. We hope this research has contributed to a better understanding of generative AI’s capacity to add value to company operations and fuel economic growth and prosperity as well as its potential to dramatically transform how we work and our purpose in society.

Second, such tools can automatically generate, prioritize, run, and review different code tests, accelerating testing and increasing coverage and effectiveness. Third, generative AI’s natural-language translation capabilities can optimize the integration and migration of legacy frameworks. Generative AI’s potential in R&D is perhaps less well recognized than its potential in other business functions.

The economic potential of generative AI: The next productivity frontier

Based on these assessments of the technical automation potential of each detailed work activity at each point in time, we modeled potential scenarios for the adoption of work automation around the world. First, we estimated a range of time to implement a solution that could automate each specific detailed work activity, once all the capability requirements were met by the state of technology development. Second, we estimated a range of potential costs for this technology when it is first introduced, and then declining over time, based on historical precedents. Unlike global private-sector tech players, government organizations simply lack the capabilities to develop foundation models while managing their risks. For example, violations of intellectual property and copyright laws can expose government agencies that own foundation models to litigation; gen AI’s occasional lack of proper source attribution makes it even harder to detect potential copyright infringement in its responses. Legal implications also apply to manipulated content—including text, images, audio, and video—that malicious actors may use to harass, intimidate, or undermine individuals and organizations.

Our analysis of the potential use of generative AI in marketing doesn’t account for knock-on effects beyond the direct impacts on productivity. Generative AI–enabled synthesis could provide higher-quality data insights, leading to new ideas for marketing campaigns and better-targeted customer segments. Marketing functions could shift resources to producing higher-quality content for owned channels, potentially reducing spending on external channels and agencies. It’s been just a year since generative AI (gen AI) tools first captured public attention worldwide. But already the economic value of gen AI is estimated to reach trillions of dollars annually—even as its risks begin to worry businesses and governments across the globe. Gen AI offers government leaders unique opportunities to steer national economic development (Exhibit 1).

Among entry-level employees, people of color perceive that their race holds them back from promotion more than White employees. Once promoted, everybody thinks their chance of getting the next promotion goes down because of their race. And the percentage of White people who feel like they’re held back goes up proportionally—equal to the share of Black people who think they’re going to be held back. However, those mitigation efforts are still in their early stages in most parts of the world, and gen AI is evolving fast, which means that governments must revise their regulations continually to keep pace.

This range implicitly accounts for the many factors that could affect the pace at which adoption occurs, including regulation, levels of investment, and management decision making within firms. Researchers start by mapping the patient cohort’s clinical events and medical histories—including potential diagnoses, prescribed medications, and performed procedures—from real-world data. Using foundation models, researchers can quantify clinical events, establish relationships, and measure the similarity between the patient cohort and evidence-backed indications. The result is a short list of indications that have a better probability of success in clinical trials because they can be more accurately matched to appropriate patient groups. Generative AI tools can facilitate copy writing for marketing and sales, help brainstorm creative marketing ideas, expedite consumer research, and accelerate content analysis and creation. The potential improvement in writing and visuals can increase awareness and improve sales conversion rates.

Lexical Semantics Oxford Research Encyclopedia of Linguistics

Moreover, some chatbots are equipped with emotional intelligence that recognizes the tone of the language and hidden sentiments, framing emotionally-relevant responses to them. Both polysemy and homonymy words have the same syntax or spelling but the main difference between them is that in polysemy, the meanings of the words are related but in homonymy, the meanings of the words are not related. In other words, we can say that polysemy has the same spelling but different and related meanings. It represents the relationship between a generic term and instances of that generic term. Here the generic term is known as hypernym and its instances are called hyponyms.

• Cdiscount, an online retailer of goods and services, uses semantic analysis to analyze and understand online customer reviews.
• The analysis of the data is automated and the customer service teams can therefore concentrate on more complex customer inquiries, which require human intervention and understanding.
• This provides a foundational overview of how semantic analysis works, its benefits, and its core components.
• Traditionally, to increase the traffic of your site thanks to SEO, you used to rely on keywords and on the multiplication of the entry doors to your site.
• Effectively, support services receive numerous multichannel requests every day.
• The analysis can segregate tickets based on their content, such as map data-related issues, and deliver them to the respective teams to handle.

While early versions of CycL were described as being a frame language, more recent versions are described as a logic that supports frame-like structures and inferences. Cycorp, started by Douglas Lenat in 1984, has been an ongoing project for more than 35 years and they claim that it is now the longest-lived artificial intelligence project[29]. To represent this distinction properly, the researchers chose to “reify” the “has-parts” relation (which means defining it as a metaclass) and then create different instances of the “has-parts” relation for tendons (unshared) versus blood vessels (shared). Figure 5.1 shows a fragment of an ontology for defining a tendon, which is a type of tissue that connects a muscle to a bone. When the sentences describing a domain focus on the objects, the natural approach is to use a language that is specialized for this task, such as Description Logic[8] which is the formal basis for popular ontology tools, such as Protégé[9]. In the case of syntactic analysis, the syntax of a sentence is used to interpret a text.

Representing variety at the lexical level

The extra dimension that wasn’t available to us in our original matrix, the r dimension, is the amount of latent concepts. Generally we’re trying to represent our matrix as other matrices that have one of their axes being this set of components. You will also note that, based on dimensions, the multiplication of the 3 matrices (when V is transposed) will lead us back to the shape of our original matrix, the r dimension effectively disappearing. If we’re looking at foreign policy, we might see terms like “Middle East”, “EU”, “embassies”.

Traditionally, to increase the traffic of your site thanks to SEO, you used to rely on keywords and on the multiplication of the entry doors to your site. A more impressive example is when you type “boy who lives in a cupboard under the stairs” on Google. Google understands the reference to the Harry Potter saga and suggests sites related to the wizard’s universe. A complier’s static analyzer only needs to check whether programs violate language rules.

Basic Units of Semantic System:

SEO Quantum is a natural referencing solution that integrates 3 tools among the semantic crawler, the keyword strategy, and the semantic analysis. Because of the implementation by Google of semantic analysis in the searches made by users. There are many semantic analysis tools, but some are easier to use than others. Sentiment analysis tools work by automatically detecting the tone, emotion, and turn of phrases and assigning them a positive, negative, or neutral label, so you know what types of phrases to use on your site. To understand semantic analysis, it is important to understand what semantics is. Is correct according to the grammar—some might even say it is syntactically correct.

In the realm of customer support, automated ticketing systems leverage semantic analysis to classify and prioritize customer complaints or inquiries. When a customer submits a ticket saying, “My app crashes every time I try to login,” semantic analysis helps the system understand the criticality of the issue (app crash) and its context (during login). As a result, tickets can be automatically categorized, prioritized, and sometimes even provided to customer service teams with potential solutions without human intervention. Conversational chatbots have come a long way from rule-based systems to intelligent agents that can engage users in almost human-like conversations. The application of semantic analysis in chatbots allows them to understand the intent and context behind user queries, ensuring more accurate and relevant responses. For instance, if a user says, “I want to book a flight to Paris next Monday,” the chatbot understands not just the keywords but the underlying intent to make a booking, the destination being Paris, and the desired date.

Linking of linguistic elements to non-linguistic elements

In the diagram below the geometric effect of M would be referred to as “shearing” the vector space; the two vectors 𝝈1 and 𝝈2 are actually our singular values plotted in this space. The semantic analysis does throw better results, but it also requires substantially more training and computation. Syntactic analysis involves analyzing the grammatical syntax of a sentence to understand its meaning. example of semantic analysis Four broadly defined theoretical traditions may be distinguished in the history of word-meaning research. The second pillar of conceptual metaphor theory is the analysis of the mappings inherent in metaphorical patterns. Metaphors conceptualize a target domain in terms of the source domain, and such a mapping takes the form of an alignment between aspects of the source and target.

A maximally general definition covering both port ‘harbor’ and port ‘kind of wine’ under the definition ‘thing, entity’ is excluded because it does not capture the specificity of port as distinct from other words. Automatically classifying tickets using semantic analysis tools alleviates agents from repetitive tasks and allows them to focus on tasks that provide more value while improving the whole customer experience. Automated semantic analysis works with the help of machine learning algorithms. However, machines first need to be trained to make sense of human language and understand the context in which words are used; otherwise, they might misinterpret the word “joke” as positive.

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These solutions can provide instantaneous and relevant solutions, autonomously and 24/7. The challenge of semantic analysis is understanding a message by interpreting its tone, meaning, emotions and sentiment. Today, this method reconciles humans and technology, proposing efficient solutions, notably when it comes to a brand’s customer service. It is the first part of the semantic analysis in which the study of the meaning of individual words is performed.

• The first part of semantic analysis, studying the meaning of individual words is called lexical semantics.
• This notion of generalized onomasiological salience was first introduced in Geeraerts, Grondelaers, and Bakema (1994).
• This study also highlights the future prospects of semantic analysis domain and finally the study is concluded with the result section where areas of improvement are highlighted and the recommendations are made for the future research.
• Moreover, it is also helpful to customers as the technology enhances the overall customer experience at different levels.

For us humans, there is nothing more simple than recognising the meaning of a sentence based on the punctuation or intonation used. Now, we can understand that meaning representation shows how to put together the building blocks of semantic systems. In other words, it shows how to put together entities, concepts, relation and predicates to describe a situation. Semantic analysis is the understanding of natural language (in text form) much like humans do, based on meaning and context.

This ends our Part-9 of the Blog Series on Natural Language Processing!

Rosch concluded that the tendency to define categories in a rigid way clashes with the actual psychological situation. Instead of clear demarcations between equally important conceptual areas, one finds marginal areas between categories that are unambiguously defined only in their focal points. This observation was taken over and elaborated in linguistic lexical semantics (see Hanks, 2013; Taylor, 2003). Specifically, it was applied not just to the internal structure of a single word meaning, but also to the structure of polysemous words, that is, to the relationship between the various meanings of a word.

This is why semantic analysis doesn’t just look at the relationship between individual words, but also looks at phrases, clauses, sentences, and paragraphs. However, reaching this goal can be complicated and semantic analysis will allow you to determine the intent of the queries, that is to say, the sequences of words and keywords typed by users in the search engines. Other necessary bits of magic include functions for raising quantifiers and negation (NEG) and tense (called “INFL”) to the front of an expression.

3.3 Frame Languages and Logical Equivalents

By correlating data and sentiments, EcoGuard provides actionable and valuable insights to NGOs, governments, and corporations to drive their environmental initiatives in alignment with public concerns and sentiments. Beyond just understanding words, it deciphers complex customer inquiries, unraveling the intent behind user searches and guiding customer service teams towards more effective responses. Pairing QuestionPro’s survey features with specialized semantic analysis tools or NLP platforms allows for a deeper understanding of survey text data, yielding profound insights for improved decision-making. It recreates a crucial role in enhancing the understanding of data for machine learning models, thereby making them capable of reasoning and understanding context more effectively.

Semantic analysis aids in analyzing and understanding customer queries, helping to provide more accurate and efficient support. Continue reading this blog to learn more about semantic analysis and how it can work with examples. Also, ‘smart search‘ is another functionality that one can integrate with ecommerce search tools. The tool analyzes every user interaction with the ecommerce site to determine their intentions and thereby offers results inclined to those intentions. Maps are essential to Uber’s cab services of destination search, routing, and prediction of the estimated arrival time (ETA). Along with services, it also improves the overall experience of the riders and drivers.

For Example, you could analyze the keywords in a bunch of tweets that have been categorized as “negative” and detect which words or topics are mentioned most often. It represents the general category of the individuals such as a person, city, etc. In the dynamic landscape of customer service, staying ahead of the curve is not just a… In the sentence «John gave Mary a book», the frame is a ‘giving’ event, with frame elements «giver» (John), «recipient» (Mary), and «gift» (book). It may be defined as the words having same spelling or same form but having different and unrelated meaning. For example, the word “Bat” is a homonymy word because bat can be an implement to hit a ball or bat is a nocturnal flying mammal also.

It is also a key component of several machine learning tools available today, such as search engines, chatbots, and text analysis software. Descriptively speaking, the main topics studied within lexical semantics involve either the internal semantic structure of words, or the semantic relations that occur within the vocabulary. Within the first set, major phenomena include polysemy (in contrast with vagueness), metonymy, metaphor, and prototypicality.

Moreover, semantic categories such as, ‘is the chairman of,’ ‘main branch located a’’, ‘stays at,’ and others connect the above entities. Semantic analysis can also benefit SEO (search engine optimisation) by helping to decode the content of a users’ Google searches and to be able to offer optimised and correctly referenced content. The goal is to boost traffic, all while improving the relevance of results for the user. As such, semantic analysis helps position the content of a website based on a number of specific keywords (with expressions like “long tail” keywords) in order to multiply the available entry points to a certain page. These two techniques can be used in the context of customer service to refine the comprehension of natural language and sentiment. This technology is already in use and is analysing the emotion and meaning of exchanges between humans and machines.

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